Using Multiple Optimisers gives Index Error?

[TheSeparatrix]

Hello,
I am trying to build a model which uses multiple optimisers. When I try to train the model I get the error validation_step() missing 1 required positional argument: 'optimizer_idx'. I have reproduced this error on the BoringModel used for bug reports:

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import os

import torch
from torch.utils.data import DataLoader, Dataset

from pytorch_lightning import LightningModule, Trainer


class RandomDataset(Dataset):
    def __init__(self, size, length):
        self.len = length
        self.data = torch.randn(length, size)

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return self.len


class BoringModel(LightningModule):
    def __init__(self):
        super().__init__()
        self.layer = torch.nn.Linear(32, 2)

    def forward(self, x):
        return self.layer(x)

    def training_step(self, batch, batch_idx, optimizer_idx):
        if optimizer_idx == 0:
            loss = self(batch).sum()
            self.log("train_loss", loss)
        if optimizer_idx == 1:
            loss = self(batch).sum()
            self.log("train_loss", loss)
        return {"loss": loss}

    def validation_step(self, batch, batch_idx, optimizer_idx):
        if optimizer_idx == 0:
            loss = self(batch).sum()
            self.log("valid_loss", loss)
        if optimizer_idx == 1:
            loss = self(batch).sum()
            self.log("valid_loss", loss)
        return {"loss": loss}

    def test_step(self, batch, batch_idx, optimizer_idx):
        if optimizer_idx == 0:
            loss = self(batch).sum()
            self.log("test_loss", loss)
        if optimizer_idx == 1:
            loss = self(batch).sum()
            self.log("test_loss", loss)
        return {"loss": loss}

    def configure_optimizers(self):
        opt_a = torch.optim.SGD(self.layer.parameters(), lr=0.1)
        opt_b = torch.optim.SGD(self.layer.parameters(), lr=0.1)
        return [opt_a, opt_b], []


def run():
    train_data = DataLoader(RandomDataset(32, 64), batch_size=2)
    val_data = DataLoader(RandomDataset(32, 64), batch_size=2)
    test_data = DataLoader(RandomDataset(32, 64), batch_size=2)

    model = BoringModel()
    trainer = Trainer(
        default_root_dir=os.getcwd(),
        limit_train_batches=1,
        limit_val_batches=1,
        limit_test_batches=1,
        num_sanity_val_steps=0,
        max_epochs=1,
        enable_model_summary=False,
    )
    trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)
    trainer.test(model, dataloaders=test_data)


if __name__ == "__main__":
    run()

What am I missing?

Thanks for the help!

[ananthsub]

Validation doesn't require optimizers. Try removing the
optimizer_idx argument from your validation_step method definition

[TheSeparatrix]

Right, that makes sense. Works fine now, thank you.